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Related Experiment Video

Updated: Sep 22, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

In-scanner head motion and structural covariance networks.

Heath R Pardoe^1,2, Samantha P Martin¹

¹Comprehensive Epilepsy Center, Department of Neurology, NYU Grossman School of Medicine, New York, New York, USA.

Human Brain Mapping

|May 20, 2022

Summary

In-scanner head motion significantly impacts brain imaging by inflating correlations in structural covariance networks. This artifact can distort findings, suggesting caution when interpreting neuroimaging studies involving head movement.

Keywords:

graph theory morphometrics quality control volumetry

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Area of Science:

Neuroimaging
Neuroscience
Radiology

Background:

In-scanner head motion is a common artifact in structural brain MRI.
Head motion can systematically reduce estimated regional gray matter volumes.
The impact of head motion on structural covariance networks is not well understood.

Purpose of the Study:

To investigate how head motion affects structural covariance networks derived from regional gray matter volumes.
To determine if head motion introduces systematic bias in network construction and analysis.
To explore methods for mitigating motion-related artifacts in structural covariance network studies.

Main Methods:

Acquired T1-weighted MRI data from 29 healthy adults under both motion-affected and low-motion conditions.
Estimated relative regional gray matter volumes using voxel-based morphometry.
Performed structural covariance network analyses by systematically varying the inclusion of motion-affected scans.

Main Results:

Increased head motion led to increased standard deviation in regional gray matter estimates.
Higher motion levels resulted in increased pairwise correlations between brain regions.
Head motion systematically altered graph theoretic metrics of structural covariance networks.
Weighting correlations by image quality metrics showed potential to mitigate motion effects.

Conclusions:

In-scanner head motion is a significant source of error in structural covariance network analysis.
Motion artifacts violate the assumption that these networks reflect true neuroanatomical connectivity.
Results from structural covariance studies, especially with mobile subjects, require cautious interpretation.